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Time-Frequency Approach Applied to Finding Interaction Regions in Pathogenic Proteins.

ABSTRACT: Protein-protein interactions govern all molecular processes for living organisms, even those involved in pathogen infection. Pathogens such as virus, bacteria, and parasites contain proteins that help the pathogen to attach, penetrate, and settle inside the target cell. Thus, it is necessary to know the regions in pathogenic proteins that interact with host cell receptors. Currently, powerful pathogen databases are available and many pathogenic proteins have been recognized, but many pathogenic proteins have not been characterized. This work developed a program in MATLAB environment based on the time-frequency analysis to recognize important sites in proteins. Our program highlights the highest energy patches in proteins from their time-frequency distribution and matches the corresponding frequency. We sought to know if this approach is able to recognize stretches residues related to interaction. Our approach was applied to five study cases from pathogenic co-crystallized structures that have been well characterized. We searched the frequencies that characterize interaction regions in pathogenic proteins and with this information tried to identify new interaction patches in either paralogs or orthologs. We found that our program generates a well-interpretable graphic under several descriptors that can show important regions in proteins even those related to interaction. We propose that this MATLAB program could be used as a tool to explore outstanding regions in uncharacterized proteins.

SUBMITTER: Arenas AF

PROVIDER: S-EPMC6552352 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Time-Frequency Approach Applied to Finding Interaction Regions in Pathogenic Proteins.

Arenas Ailan F AF Arango-Plaza Nicolás N Arenas Juan Camilo JC Salcedo Gladys E GE

Bioinformatics and biology insights 20190604

Protein-protein interactions govern all molecular processes for living organisms, even those involved in pathogen infection. Pathogens such as virus, bacteria, and parasites contain proteins that help the pathogen to attach, penetrate, and settle inside the target cell. Thus, it is necessary to know the regions in pathogenic proteins that interact with host cell receptors. Currently, powerful pathogen databases are available and many pathogenic proteins have been recognized, but many pathogenic ...[more]

PMID: 31210729

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Project description:BackgroundAn important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches.ResultsWe propose a novel approach of finding correlated short sequence motifs from protein-protein interaction data to effectively circumvent the above-mentioned limitations. Correlated motifs are those motifs that consistently co-occur only in pairs of interacting protein sequences, and could possibly interact with each other directly or indirectly to mediate interactions. We adopted the (l, d)-motif model and formulate finding the correlated motifs as an (l, d)-motif pair finding problem. We present both an exact algorithm, D-MOTIF, as well as its approximation algorithm, D-STAR to solve this problem. Evaluation on extensive simulated data showed that our approach not only eliminated the need for any prior protein grouping, but is also more robust in extracting motifs from noisy interaction data. Application on two biological datasets (SH3 interaction network and TGFbeta signaling network) demonstrates that the approach can extract correlated motifs that correspond to actual interacting subsequences.ConclusionThe correlated motif approach outlined in this paper is able to find correlated linear motifs from sparse and noisy interaction data. This, in turn, will expedite the discovery of novel linear binding motifs, and facilitate the studies of biological pathways mediated by them.

Dataset Information

Time-Frequency Approach Applied to Finding Interaction Regions in Pathogenic Proteins.

Publications

Time-Frequency Approach Applied to Finding Interaction Regions in Pathogenic Proteins.

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